1. Field of the Invention
The present invention relates to an image retrieval apparatus and a method for determining the similarity of each image, and retrieving an image similar to a specified image, and more specifically to an image retrieval system for computing the similarity when the configurations of the feature vector of a query image and the feature vector of an image to be compared are different from each other especially when a retrieving process is performed by using an image feature.
2. Description of the Related Art
Conventionally, an image retrieving process has been performed by converting the feature of an image into data, indicating an image feature vector, determining the similarity of each image by using the image feature vector, and retrieving a similar image (for example, from accumulated images). Various apparatuses and computer programs have been developed to perform the process.
In the above mentioned conventional technology for retrieving an image, a method of retrieving a similar image based on the similarity of an image feature vector is adopted using a feature vector extracted from an image.
FIG. 8 shows an image retrieval system using the conventional image feature vector. FIG. 8 is a block diagram showing a configuration of the conventional image retrieval system. The conventional image retrieval system comprises first image feature vector generation means 102, second image feature vector generation means 202, image feature vector similarity computation means 107, a similarity evaluation unit 109, and an output device 111 for outputting a retrieval result.
In this system, it is necessary to determine in advance what feature vector is to be extracted in what configuration to compare the image feature vector of a query image with image feature vectors of images to be retrieved. First, the first image feature vector generation means 102 generates an image feature vector 103 from a query image 101 and query image feature vector configuration information 100 describing the configuration of the image feature vector. The second image feature vector generation means 202 generates an image feature vector 203 from images 201 to be retrieved and the images-to-be-retrieved feature vector configuration information 200 describing the configuration of the images-to-be-retrieved feature vector. It further comprises the image feature vector similarity computation means 107 for computing similarity 108 between the image feature vector 103 and the image feature vector 203, a similarity evaluation unit 109 for evaluating the similarity based on the computed similarity 108 and returning a retrieval result 110, and the output device 111 for outputting the retrieval result.
On the other hand, as an example of the conventional technology, the conventional image retrieval system using the frequency distribution feature vector expressed by the color layout of an image using a frequency area as an image feature vector is disclosed in Japanese Patent Laid-Open Publication No. 2000-259832. The frequency distribution feature vector refers to an image feature vector indicating the energy of each band obtained by dividing the frequency distribution of a spectrum of the color (average color) of an image into a plurality of bands and performing a frequency analysis. The configuration information describes a band used as a frequency distribution feature vector. When the configuration of the frequency distribution feature vector of a query image is different from the configuration of the frequency distribution feature vector extracted from images to be retrieved, that is, when a query image feature vector has 18 conversion coefficients (6 coefficients of luminance and 6 coefficients of chrominance) when the images-to-be-retrieved feature vector has 12 conversion coefficients (6 coefficients of luminance and 3 coefficients of chrominance), etc., there has been no means for computing the similarity of them. Thus, to retrieve an image in an environment in which image feature vectors having plural types of configurations exist, an image feature vector database and similarity computation means are required corresponding to the configuration of the feature vector of a query image. Therefore, it is necessary to provide similarity computation means and an image feature vector database corresponding to each type of configuration of an image feature vector. Therefore, such a problem arises that the apparatus is complicated and costly for manufacturing.
On the other hand, there is a method of solving the above mentioned problem by comparing only the common coefficients and computing the similarity. In this case, the dimension of the computed similarity depends on the configuration of the coefficient of an image, the comparison cannot be performed using the same similarity. For example, if a query image has 18 coefficients, and the image feature vectors of images to be retrieved are 12 and 18 coefficients, then the similarity computed by comparing only 12 coefficients and the similarity computed by comparing 18 coefficients have different weights. Therefore, the similarity cannot be evaluated on the same basis.
As described above, there have been the following problems with the conventional apparatus.
First, there are a number of elements and configurations forming the image feature vector indicating the features of an element. To compare and retrieve images, it is necessary to prepare image feature vectors having common configurations for each of the images to be compared (retrieved).
Second, there is a method of solving the above mentioned problem by comparing common components only and computing the similarity. However, in this case, since the dimension of the computed similarity depends on each image, the comparison cannot be carried out by using the same similarity. That is, the comparison cannot be carried out on images having different coefficients and feature vectors.